Abstract

Spatial joins are join operations that involve spatial data types and operators. Due to some basic properties of spatial data, many conventional join processing strategies suffer serious performance penalties or are not applicable at all in this case. In this paper we explore which of the join strategies known from conventional databases can be applied to spatial joins as well, and how some of these techniques can be modified to be more efficient in the context of spatial data. Furthermore, we describe a class of tree structures, called generalization trees, that can be applied efficiently to compute spatial joins in a hierarchical manner. Finally, we model the performance of the most promising strategies analytically and conduct a comparative study. Parts of this work have been carried out while the author was visiting the International Computer Science Institute and the University of California at Berkeley. 1 1 Introduction Spatial databases have become a very active research top...

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